Interior thermal insulation is frequently one of the only possible solutions for thermal upgrade of the building envelope where the external appearance cannot be changed. In this study, four insulation materials were used in a case study in a historical school building in in situ test walls. The indoor climate in the test room was controlled to simulate the typical dwelling with high moisture load. The temperatures, relative humidity, and heat flows were monitored over 9 months to analyze the hygrothermal performance of four different insulation materials. The hygrothermal performance of insulation materials during drying and wetting periods are presented. Moisture test reference year was used in working out possible energy-renovation solutions. The results show that timing of the renovation works is a matter of consideration to avoid the hygrothermal risks inside the renovated wall assemblies. The results show that in all the cases, thermal comfort can be improved by increasing the inner surface temperature and decreasing thermal conductivity. However, in some cases, the risks of mold growth and interstitial condensation were present inside the retrofitted wall assemblies. Computer simulations of the wall assemblies with moisture reference years under different humidity loads concluded that all solutions are suitable for future analysis.
Hygrothermal simulations are of major importance for critical problems in building physics, such as the application of internal insulation in heritage buildings. These simulations require numerous material parameters that are challenging to determine. We present measurements of typical internal insulation materials, calcium-silicate and autoclaved aerated concrete, which we expose to a warm, humid climate on one side and a cold temperature on the other side. We measure the moisture gain over time and determine the moisture profile at experiment end. In an inverse modelling approach, the measurements are used to identify material parameters, in particular vapour conductivity and capillary conductivity as a function of moisture content. We found the measurements of crucial importance for the accurate determination of these parameters. When the parameters rely only on isothermal measurements such as the drying experiment, the model fails to predict the capillary condensation process. We demonstrate this on a dataset from another study with interior insulation subjected to changing boundary conditions. The model calibrated with capillary condensation data reliably reconstructs measurements while the drying-calibrated model drastically underestimates the moisture content.
Reliable hygrothermal modelling depends on the quality of material characterization, especially so when higher moisture contents are concerned. Previous research has shown that adding additional material tests (e.g. capillary condensation redistribution (CCR) test) to the experimental dataset brings improvements to the modelling accuracy, but also adds to the workload of characterization process. This paper discusses a generative optimization workflow to increase the speed of the characterization and quality of the result. The proposed workflow incorporates optimization tool GenOpt and hygrothermal modelling software IBK Delphin to search for best fit of the water vapour and liquid conductivity curves of interior insulation materials based on modelling the CCR, drying and wet cup tests. Finally, models using material data from the proposed workflow and from the software database are compared to measurement results from two studies on interior thermal insulation. The results suggest that the generative optimization shows promise on the grounds of reducing tedious work analysing material tests. Also, a wider experimental dataset is shown to be useful when characterizing the vapour and liquid conductivity functions in over-hygroscopic region.
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